Fast and Near-Optimum Schedule Optimization for Large-Scale Projects
Bibliographic record
Abstract
Real-life construction projects are large in size and are challenged by many constraints, including strict deadlines and resource limits. In this paper, constraint programming (CP) is used as an advanced mathematical technique that suits schedule optimization problems. A practical CP optimization model has been developed to resolve both deadline and resource constraints simultaneously in large-scale projects. The proposed CP model is much faster than metaheuristic techniques and provides a set of feasible project durations that do not violate resource limits. The paper compares the CP results with several case studies from the literature to prove the practicality and usefulness of the CP approach to both researchers and practitioners. The CP model of this paper could provide solutions within 6.5% deviation from optimum schedules for a large project of 2,000 activities within minutes of processing time. This paper thus contributes to introducing a superior optimization model that is suitable for large-size projects and helps to render schedule optimization a mainstream cost-saving function within commercial scheduling systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".